Press

Like the multi-purpose pocket tool, artificial intelligence is adding value across diverse industries from healthcare to financial services.

In 2016, rats beset parts of Bishan, much to the chagrin of its residents, who encountered the unwelcome furry visitors in food centres, shopping malls and housing areas.

If you’ve never thought of pest control as a high-tech endeavour, think again. The authorities, not content to stick to traditional methods, roped in SmartCow, a Singapore startup specialising in industrial-grade artificial intelligence (AI) hardware, to help rid the area of the infestation.

To track the rodents’ movements, SmartCow attached thermal cameras to the undersides of drain covers in affected areas. It then used data analytics to automatically examine the collected images and determine the pests’ most-used underground routes.

“The pest controller was able to use the information to strategically place poison to kill the rats, saving manpower and resources,” recalls Ms Annabelle Kwok, SmartCow’s founder and CEO.

Like the Swiss Army knife, a clever gadget that can be used in all sorts of situations, the potential applications of AI across various sectors—including one as unexpected as pest management—is why startups and large enterprises alike are developing and adopting AI-based technologies.

Singapore is home to a burgeoning AI research and development scene. Recent figures from publishing giant Elsevier show that publications on the topic from Singapore are the second most highly cited in the world, behind Switzerland.

In addition, the Singapore government in May 2017 launched AI.SG, a S$150 million, five-year programme to fund projects that use AI to solve real-world problems, particularly those related to finance, healthcare and city management.

Automation gets an intelligence boost

Humans have for decades relied on machines to carry out routine grunt work. But with AI now enabling machines to handle more complicated tasks, this automation can be taken a step further. For instance, while most manufacturers still use manpower to monitor production lines for defects and errors, a few cameras coupled with data analytics software can do the job for a fraction of the cost, and with higher accuracy.

A large semiconductor firm in Singapore now uses another of SmartCow’s products—a portable device loaded with customised AI software that connects to cameras and sensors—to inspect its printed circuit boards.

“Our systems cost less than S$2,000 each, and can analyse images from cameras to detect whether any component is missing or placed in the wrong way, even if it’s just one millimetre to the left, rotated in the wrong direction or partially lifted from the board,” says Ms Kwok. “It can also generate reports on the percentage of components that are faulty, so that companies have that useful information when they are considering or negotiating with suppliers,” she adds.

Picking up the pace with autonomous robots

Although robots can now perform a wide range of activities, most still lack the ability to move around autonomously, says Mr Abhishek Gupta, co-founder and CEO of Singapore robot navigation startup Movel AI. Yet, autonomous robots would be tremendously useful, Mr Gupta points out. Logistics and construction firms, for example, could use automated ground vehicles and drones to transport goods and inspect work sites, reducing labour costs and improving safety. Self-navigating robots would also find many applications in homes, hospitals and other non-industrial settings, he says.

Stepping up its efforts in robot navigation, Movel AI is building advanced systems based on computer vision and software that can combine inputs from a number of different sensors, says Mr Gupta.

“Compared to the more common light detection and ranging (LIDAR) and radar navigation systems, ours is more lightweight and cost-effective, and provides robots with a better understanding of their surroundings,” he explains.

Several restaurants in Singapore are already using robots equipped with the company’s system to collect dirty dishes from dining tables, Mr Gupta shares. Movel AI is also discussing opportunities to deploy its technology with various other companies, including several that develop automated drones and vehicles, and a security corporation that wants to use robots for patrols, he adds.

Transforming the traditional

AI can also benefit industries where no heavy physical labour is involved—retailers, for example, can refine the way they stock their inventory.

“Traditional inventory systems can only tell retailers how many units of a product were sold. With deep learning models, we can identify customers’ characteristics, such as age and gender, to tailor promotions,” says Ms Kwok. “With computer vision, we can even measure the ‘bounce rate’ of physical items. If a particular handbag was picked up many times in the past two hours but not a single unit was sold, for example, that may signal to the retailer that its price needs to be lowered,” she adds.

Banks and financial services firms can improve their operations and bottom line through the use of AI programmes that better calculate the risks of providing financial services and products, says Mr Drew Perez, founder of Singapore-based start-up Adatos, which provides data intelligence solutions related to customer analysis, portfolio optimisation and data blending.

A large regional consumer bank in Southeast Asia used Adatos’s portfolio optimisation product to better assess the probability that retail customers would be late or default on loan payments.

“In the Asia Pacific region, there are markets where the majority of the population remains unbanked, and a national credit bureau is either not present or unable to provide accurate risk scores due to a lack of data from traditional sources,” adds Mr Perez.

Using data intelligence for risk analysis enables financial services firms to analyse non-traditional sources of data, such as mobile telephone credit ‘loads’, to get an accurate risk analysis before providing services to such previously untapped markets.

Despite the obvious potential of AI and robotics systems, obstacles to their widespread adoption remain. One of these is a lack of qualified data scientists, says Mr Perez.

“Talent in deep learning is scarce worldwide, and high demand for existing talent has inflated data scientists’ salaries to the point where only tech giants can afford them. Most qualified data scientists are immediately hired by the likes of Google and Baidu,” he explains.

Artificial intelligence in various forms is not a panacea for every problem, but it can help enterprises fend off cyber attacks and get better at what they do.

Former US intelligence officer Drew Perez is an old hand at making sense of vast volumes of data using machine learning and artificial intelligence (AI) in the name of counter-terrorism and national security.

Marketers should not waste their time or money hiring data scientists as the information businesses are collating is often not good enough to justify their high salaries, a former US intelligence officer and data analyst has said.

Drew Perez: “This is not Terminator Judgement Day”

Drew Perez, founder of tech firm Adatos and a 26-year veteran of the US Department of Defense, also warned marketers not to view machines, and more specifically artificial intelligence, with trepidation and stressed machines are no substitute for humans when it comes to creativity.

“This is not Terminator Judgement Day,” he told delegates at the Mumbrella Finance Marketing Summit. “It’s up to us humans what we let machines do.”

Perez, in an address titled Rise of the Machines, said too many businesses have “dirty” data, telling the conference that a data scientist “is the last person you need to hire”.

“Do not hire a data scientist despite what the Harvard Business Review says,” he said. “It is said to be the sexiest job in the 21st Century. If you want to be sexy, go for it.

“But the reason [for not hiring a data scientist] is that they don’t scale. Second, if the data is crap, dirty, not indicative – and just because you have a lot of data does not mean that it’s indicative or relevant – they don’t have a job.”

He said data scientists are paid salaries of US$200,000 or US$500,000 “but they can’t do anything” but clean the data and get it ready to “train the machine”.

“Artificial intelligence is basically this. You train a machine to think,” Perez said. “To do that you had better be a good parent and send your child to the best school.

“Send them to a prep school and give them a crap education is going to give you crap, and that is the elephant in the room.

“Because you hire a data scientist does not mean you are going to get anything but crap if your data is crap.”

Asked later to clarify his comments, Perez said scientists were not a waste of money, but repeated his assertion that they “don’t have work to do if your data is not indicative”.

When companies receive data there are three scenarios, he said.

Perez: “There’s a morality to machines as well”

“Is the data rich enough to be indicative, and if it’s not you’ve got nothing. Maybe it is, but you may have to beg, borrow and, if you’re a government, steal to enrich it. The third outcome is yes, you do have something and then you can do something with a data scientist.

“But for the most part…it is highly probably that you don’t have indicative data.”

Perez warned marketers on two fronts: not to distrust machines or think they will solve creative problems.

“If you don’t trust the machines you are not in the game. If you think it’s all Terminator Judgement Day you are missing the point,” he said. “But don’t misunderstand it or have the wrong expectation because if you want AI to be creative in an artistic way, this is not what machines are good for.

Use machines for what they are good for, and that is menial and repetitive tasks at extremely low cost. Use humans for what we are good for. It’s a partnership. If you know how to blend it to your advantage you have a winning team.”

Perez also stressed that companies have a responsibility not to abuse the power of machines at the expense of the workforce.

“Whenever I train someone in my company the last thing I go through is the morality,” he said. “Yes I can deploy a box to replace a call centre with 10,000 trunk calls but is it responsible for me to deploy technology like that that displaces 10,000 jobs in a developing nation?